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source("./tianfengRwrappers.R")

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NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
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clusterProfiler v3.14.3  For help: https://guangchuangyu.github.io/software/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.
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    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see
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# plan("multiprocess",workers = 8)

#冠状动脉

human_coronary_countmatrix <- read.csv("GSE131778_human_coronary_scRNAseq.txt", sep = "\t")
func <- function(s) {
  paste0(strsplit(s, ".", fixed = T)[[1]][2], "_", strsplit(s, ".", fixed = T)[[1]][1])
}
colnames(human_coronary_countmatrix) <- lapply(colnames(human_coronary_countmatrix), func) # 拆分样本

颈动脉斑块 CA dataset1

# 批量读取计数矩阵
# 需要把行名的gene删掉，用vscode修改
count_mats <- list.files("./CA_GSE155512")
count_mats <- count_mats[count_mats != "sampleinfo.txt"]
allList <- lapply(count_mats, function(folder) {
  CreateSeuratObject(
    counts = read.csv(paste0("./CA_GSE155512/", folder), sep = "\t"),
    project = folder, min.cells = 10, min.features = 300
  )
})
# 合并seurat对象
CA_dataset1 <- merge(allList[[1]],
  y = allList[-1], add.cell.ids = count_mats,
  project = "CA_dataset1"
)
rm(allList)

CA_dataset1 <- PercentageFeatureSet(CA_dataset1, pattern = "^MT-", col.name = "percent.mt") %>%
    subset(subset = nFeature_RNA > 600 & nFeature_RNA < 6000 & nCount_RNA > 1000 &  nCount_RNA < 30000) %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)

颈动脉斑块 CA dataset2

CA_dataset2 <- CreateSeuratObject(Read10X("./CA_GSE159677/"), names.field = 2, names.delim = "-",
                                     project = "CA_dataset2", min.cells = 10, min.features = 300) %>% 
    PercentageFeatureSet(pattern = "^MT-", col.name = "percent.mt") %>%
    subset(subset = nFeature_RNA > 600 & nFeature_RNA < 6000 & nCount_RNA > 1000 &  nCount_RNA < 30000) %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)

添加metadata samples存储完整信息，conditions按区域分，groups按病例分

Idents(human_coronary) <- human_coronary$orig.ident
Idents(human_coronary) <- c("1","1","2","2","3","3","4","4")
human_coronary$samples <- Idents(human_coronary)
Idents(human_coronary) <- human_coronary$seurat_clusters

Idents(CA_dataset2) <- CA_dataset2$orig.ident
CA_dataset2 <- RenameIdents(CA_dataset2,'1' = 'AC_1','2' = 'PA_1','3' = 'AC_2','4' = 'PA_2','5' = 'AC_3','6' = 'PA_3')
UMAPPlot(CA_dataset2)

CA_dataset2$sample <- Idents(CA_dataset2)
CA_dataset2 <- RenameIdents(CA_dataset2,'AC_1' = 'AC','PA_1' = 'PA','AC_2'= 'AC','PA_2'= 'PA','AC_3'= 'AC','PA_3'= 'PA')
CA_dataset2$conditions <- Idents(CA_dataset2)
Idents(CA_dataset2) <- CA_dataset2$orig.ident
CA_dataset2 <- RenameIdents(CA_dataset2, '1' = 'sp_1','2' = 'sp_1','3' = 'sp_2','4' = 'sp_2','5' = 'sp_3','6' = 'sp_3')
CA_dataset2$groups <- Idents(CA_dataset2)
Idents(CA_dataset2) <- CA_dataset2$seurat_clusters

保存结果

saveRDS(human_coronary,"human_coronary.rds")
saveRDS(CA_dataset1,"CA_dataset1.rds")
saveRDS(CA_dataset2,"CA_dataset2.rds") #已经经过分组处理了

---

读取结果

human_coronary <- readRDS("human_coronary.rds")
CA_dataset1 <- readRDS("CA_dataset1.rds")
CA_dataset2 <- readRDS("CA_dataset2.rds") #已经经过分组处理了

修改分群

基质细胞分类

ECs亚群分析 整合

整合算法可能出现负值，运行SCENIC时舍弃了这些异常值

# 提取内皮细胞亚群
ECs_list <- list(subset(CA_dataset1, idents = "Endothelial"), subset(human_coronary, idents = "Endothelial"))

ECs_list <- lapply(X = ECs_list, FUN = function(x) {
  x <- NormalizeData(x)
  x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
# 需要分析的差异基因
int_features <- SelectIntegrationFeatures(object.list = ECs_list)
# 选择合并的anchor特征
int_anchors <- FindIntegrationAnchors(object.list = ECs_list, anchor.features = int_features)

# 根据anchor合并
ECs_combined <- IntegrateData(anchorset = int_anchors)

DefaultAssay(ECs_combined) <- "integrated"
rm("ECs_list", "int_features", "int_anchors")

multi_featureplot(c("TNFRSF11B","ACTA2","CNN1","LUM"),human_coronary)

multi_featureplot(c("TNFRSF11B","ACTA2","CNN1","LUM"),CA_dataset1)

multi_featureplot(c("TNFRSF11B","ACTA2","CNN1","LUM"),CA_dataset2)